Towards Large Scale 3D Reconstruction from Images

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Towards Large Scale 3D Reconstruction from Images"

By

Mr. Runze ZHANG


Abstract

A complete system of 3D reconstruction from images contains two main 
components: Structure-from-Motion(SfM) and Multiple View Stereo(MVS). The first 
component, SfM, recovers camera poses of each image and sparse point positions, 
and the second component, MVS, recovers 3D representation of scenes or objects 
in the images. Towards the large scale 3D reconstruction from images, the state 
of the arts of SfM and MVS suffers from scalability due to memory resources. In 
this thesis, we propose a systematic approach to accommodate the visual 
reconstruction scalability for very large scale data-sets.

The large scale optimization is fundamental for Structure-from-Motion. To break 
through the memory limitation for the large scale global bundle adjustment in 
SfM, we first propose a distributed method based on space division with the 
ADMM algorithm, so that the whole SfM can be computed in a distributed manner. 
Then, we design a new positional measurement fusion method as the application 
of the large scale optimization to utilize available positional measurements 
from other sources to improve the accuracy of camera poses.

Multiple View Stereo algorithms require to select and cluster images from large 
scale redundant image sets, so that they can process the most suitable images 
under the memory limitation. In this thesis, similar with the proposed 
distributed optimization framework, we propose a space division based method to 
select and cluster images to obtain high quality dense point clouds in the 
dense reconstruction.

All these contributions are critical to the modern 3D reconstruction in very 
large scale context, and have been demonstrated in large collection of public 
and private data-sets. By the proposed large scale optimization framework in 
SfM, and the image selection and clustering method for MVS, we can handle large 
scale image data-sets in a fully distributed manner in 3D reconstruction to 
produce accurate 3D representations automatically and efficiently.


Date:			Friday, 8 June 2018

Time:			4:30pm - 6:30pm

Venue:			Room 5560
 			Lifts 27/28

Chairman:		Prof. Yaping Gong (MGMT)

Committee Members:	Prof. Long Quan (Supervisor)
 			Prof. Pedro Sander
 			Prof. Chiew-Lan Tai
 			Prof. Michael Wang (MAE)
 			Prof. Wenping Wang (Comp Sci, HKU)


**** ALL are Welcome ****